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Nonlinear Modeling of Small Unmanned Helicopters Based on Double Neural Networks

机译:基于双神经网络的小型无人直升机的非线性建模

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This paper proposes a double neural network modeling method by studying the modeling problem for small unmanned helicopters. Based on the traditional neural network modeling method, the error compensation model is introduced, and the height control identification model of small unmanned helicopter based on BP network is established. Finally, the effectiveness and feasibility of the proposed method are verified by simulation experiments compared with traditional modeling methods. The simulation results show that the method of double neural network modeling greatly improves the approximation accuracy and generalization ability of the model.
机译:通过研究小型无人直升机的建模问题,提出了一种双神经网络建模方法。在传统神经网络建模方法的基础上,引入误差补偿模型,建立了基于BP网络的小型无人机高度控制辨识模型。最后,通过仿真实验与传统建模方法进行比较,验证了该方法的有效性和可行性。仿真结果表明,双神经网络建模方法极大地提高了模型的逼近精度和泛化能力。

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